Summary
Dr Anja Tremper is a Research Fellow in the Environmental Research Group where she is a member of the Aerosol Science Team. Her research focuses on the physical and chemical characterisation of ambient particulate matter and identifying its sources.
In her PhD research, she assessed the impact of metals associated with ambient particulates on mosses at roadside, which sparked her interest in the characterisation of particulate matter and understanding and identifying pollution sources.
The research work she is involved with is based on both the use of filter measurements and off-line analytical methods, as well as high time resolved online measurements (such as ACSM, XACT XRF). The measurement data has been used to quantify specific sources (e.g. levoglucosan as wood burning tracer in London); and to feed source apportionment methods using mass closure models and positive matrix factorisation (e.g. PMF analysis of particle size distribution near airports).
Within the Environmental Research Group her work further includes running one of the London Atmospheric Observatories or ‘supersites’, which hosts a wide range of gaseous and aerosol measurement equipment for atmospheric science and health studies.
Key Research Interests:
Measurement of atmospheric aerosol and gaseous composition
Source apportionment of aerosol sources for health impact and policy development
Assessment of new measurement techniques for aerosols and gases
Publications
Journals
Baerenbold O, Meis M, Martínez‐Hernández I, et al. , 2023, A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution, Environmetrics, Vol:34, ISSN:1180-4009
Trechera P, Garcia-Marlès M, Liu X, et al. , 2023, Phenomenology of ultrafine particle concentrations and size distribution across urban Europe, Environment International, Vol:172, ISSN:0160-4120, Pages:1-17
Chen G, Canonaco F, Tobler A, et al. , 2022, European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets, Environment International, Vol:166, ISSN:0160-4120
Manousakas M, Furger M, Daellenbach KR, et al. , 2022, Source identification of the elemental fraction of particulate matter using size segregated, highly time-resolved data and an optimized source apportionment approach, Atmospheric Environment: X, Vol:14, ISSN:2590-1621
Tremper A, Jephcote C, Gulliver J, et al. , 2022, Sources of particle number concentration and noise near London Gatwick Airport, Environment International, Vol:161, ISSN:0160-4120